Shifting more‐than‐human relationships amidst social–ecological disturbance
Bibliographic record
Abstract
Abstract Social–ecological disruptions, such as changing climate, extreme weather‐related events and the COVID‐19 pandemic, can have cascading and long‐term consequences for people, ecosystems and multispecies relationships. As the early COVID‐19 pandemic disrupted people's lives through isolation and restricted human contact, more‐than‐human relationships played a heightened role in individuals' day‐to‐day lives with potential long‐term impacts on multispecies justice. We analysed 72 interviews conducted during the early (May–June 2020) COVID‐19 lockdown in the United States to investigate how social–ecological disruptions and spatial re‐orderings, exemplified by the pandemic, reassemble more‐than‐human relationships. We consider new relational values through a transformative multispecies justice framing, which contends that times of uncertainty can inspire meaningful connections with the more‐than‐human world, facilitating care and reciprocal relationships during times of disruption. Among interviewee accounts, we find that disorderings of daily life during the pandemic interweave with past and ongoing experiences of inequity to form mosaics of disruption. These mosaics of disruption created circumstances in which interviewees formed new connections with the more‐than‐human world. The more‐than‐human connections of interviewees sat along a spectrum and did not universally represent the same strength of relational values. The more‐than‐human connections were defined by individual's positionality and restricted geographies of the circumstances. However, the newly formed relationships seemed to be ephemeral, indicating that they would not necessarily endure outside of an early‐pandemic context. Thus, while individuals reported rearranged relationships out of pandemic precarity, their transitory qualities do not directly promise long‐term transformational multispecies connections. Our findings suggest that moments of disruption alone do not necessarily produce durable change and there is a need to go beyond merely recognizing relationality. Policy implications : Transformative multispecies justice requires long‐term, routine commitment to deepening relationships with the more‐than‐human world. While future social–ecological and spatial disturbances can be a window of opportunity to initiate multispecies relationships, future initiatives and policies must actively support and foster these relationships and strong relational values beyond the disturbances—recognizing the long‐term, non‐linear processes of transformation needed to address our future challenges. Read the free Plain Language Summary for this article on the Journal blog.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".